The palm oil industry has always been a pivotal sector on the world's vegetable crop industry, especially for southeast Asian countries such as Indonesia and Malaysia. These fast-growing sectors has led to challenges for assessing oil palm fruit maturity. In order to overcome these obstacles, this research suggests an automating the detection of oil palm fruit maturity through the use of computer vision. The six maturity clusters for oil fruit images are Rotten, Underripe, Unripe, Overripe, and Empty Bunch. Several different types of YOLOv8 models, including YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x, are evaluated in this study. The YOLOv8m emerges as the best option, which shows notable gains in accuracy metrics at the same time as processing speeds. For example, YOLOv8m processes at an average speed of about 14.68 iterations per second and achieves top-1 and top-5 accuracy of 0.98 and 0.998, respectively.
